"No one is harder on a talented person than the person themselves" - Linda Wilkinson ; "Trust your guts and don't follow the herd" ; "Validate direction not destination" ;

September 17, 2021

Driver Profiling - Telematics - Connected Cars

Sometimes we need more data pointers, research paper reads for a use case. arxiv / phd papers are a wealth of information to get right directions for dataset, features, models

Paper - Telematics and Contextual Data Analysis and Driving Risk Prediction

Key Notes

  • Usecase - Driving Risk Prediction
  • Solution that consists of three parts
  • a) characterizing driving context, 
  • b) characterizing driving style, and 
  • c) context-aware driving risk prediction

Areas

  • Driving context can be described as a combination of location (e.g., Interstate-90) and time (e.g., weekdays between 3pm to 7pm).
  • Characteristics of contexts from the aggregate behavior of drivers Segmenting trajectories to identify meaningful driving patterns
  • Analyze each pattern with respect to contextual data to identify cause-andeffect patterns of significance

Features

  • Contextual data - traffic data
  • Weather data - weather stations
  • Road-network characteristics - (e.g., road type and road shape)

Analysis of telematics alongside contextual data provides valuable insights regarding an individual’s behavior, common driving habits, and characteristics of the road network with regard to dynamic traffic flow


  • Roads Analysis - sharp-turn , smooth-turn, exit/merge, intersection, exit/merge, ramp, bridge
  • Drive Analysis - speed, acceleration, GPS coordinates, heading
  • Time features - Type of Day, Time of the Day, frequency of congestion events
  • For traffic data, we have loc = (latitude, longitude, Street Name, Street Side, Zipcode, City, State)

Patterns

  • Monthly Traffic Distribution
  • Monthly Weather Distribution
  • Weekly Traffic Distribution

Weather Entity

  • Severe-Cold: the case of having extremely low temperature, with temperature 
  • Fog: the case where there is low visibility condition as result of fog or haze.
  • Hail: the case of having solid precipitation including ice pallets and hail.
  • Rain: the case of having rain, including any type of the rain, ranging from light to heavy.
  • Snow: the case of having snow, including any type, ranging from light to heavy.
  • Storm: the extremely windy condition, where the wind speed is at least 60kmh.
  • Precipitation: a generic label which we frequently observed in raw weather data, however, we have no further information to include them in any of the previously described entity types

Road-network - Interstates and Freeways, Cities

Clustering based on Traffic patterns

Study the behavior of an individual driver in order to evaluate how risky or safe he/she is

Common propagation patterns of traffic and weather entities

rain → accident → congestion

major construction → more congestions

Tree-pattern-mining-based process, which we name short-term pattern discovery

Input: A trajectory T.

Model: A predictive model M to capture variations in driving behavior to derive driving style information


gps, accelerometer, and magnetometer

Contextual  data such as traffic events, weather data, points-of-interest, and time

Comprehensive set of attributes to describe each accident including location data, time data, natural language description of event, weather data, period-of-day information22, and relevant points-of-interest data

Dataset - US Accidents

Dataset which is called the 100-car naturalistic driving study



Cluster based on accident history leveraging accident datasets

Using telematics data alone for driving risk prediction is a recent trend given notable attention in the past few years

Risk assessment for individual drivers, based on crash and near-crash

  • (CNC) events, as well as critical-incident events (CIE), age, and personality of drivers to be the important risk factors 
  • Using K-Means clustering, they performed clustering of CNC rates, and identified three clusters of low, moderate, and high risk drivers
  • Average monthly drive time, age, gender, living region, and car’s age, to predict the frequency of claims for different drivers
  • Coarse-grained attributes such as yearly distance, number of trips, average Distance per trip, and coverage of different road types by distance
  • Driving state variables (e.g., sharp-turn, lane-change, abnormal acceleration/deceleration, and speeding with respect to speed-limit data)
  • (a) Smooth turn (b) Sharp and smooth turn (c) Sharp turn trajectories


Very Good Phd Document :)

Loved it, A lot of inspiration to apply it in use cases.

More reads

Happy Reading!!!

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